optimizer_kickout_powell Class Reference

Powell scheme optimizer with repeated kickouts for likelihoods. More...

#include "optimizing/optimizer_kickout_powell.h"

Collaboration diagram for optimizer_kickout_powell:

Public Member Functions
	optimizer_kickout_powell (int seed)
	Class constructor, accepts the integer seed for a random number generator as an argument.
std::vector< double >	run_optimizer (likelihood &L, int dof_estimate, std::string optimizer_results_filename="Opt.dat", size_t number_of_instances=0, size_t number_of_restarts=2, double tolerance=1e-10)
	Function to run the optimizer, takes a likelihood object, vector of priors, name of an optimizer output file, and tuning parameters for the optimizer. More...
std::vector< double >	run_optimizer (likelihood &L, int dof_estimate, std::vector< double > start_parameter_values, std::string optimizer_results_filename="Opt.dat", size_t number_of_instances=0, size_t number_of_restarts=2, double tolerance=1e-10)
	Function to run the optimizer, takes a likelihood object, vector of priors, name of an optimizer output file, and tuning parameters for the optimizer. More...
std::vector< double >	run_optimizer (likelihood &L, int dof_estimate, std::vector< std::vector< double > > start_parameter_values, std::string optimizer_results_filename="Opt.dat", size_t number_of_restarts=2, double tolerance=1e-10)
	Function to run the optimizer, takes a likelihood object, vector of priors, name of an optimizer output file, and tuning parameters for the optimizer. More...
void	set_cpu_distribution (int num_likelihood)
	Function to set the distribution of processors in different layers of parallelization. More...
void	set_kickout_parameters (double kickout_loglikelihood_reduction_factor=10.0, size_t kickout_itermax=20, size_t kickout_rounds=20)
	Function to set the meta-parameters associated with the kickout procedure. More...

Private Member Functions
std::vector< double >	generate_start_point (likelihood &L)
	Function to generate a random starting point given the bounds on the prior. Assumes an arctan distribution if both bounds on the prior are infinity, an exponential distribution if the bound on one side is infinity, and a uniform distribution if both bounds are finite. More...
double	get_optimal_point (std::vector< double > &pvec, double tolerance, int itermax=0)
	Function to ncapsulate the process of optimization. Returns the maximized likelihood and resets pvec to the optimal position. More...
double	func (double p[])
double	tempered_func (std::vector< double > p, double prior_edge_beta=1.0)
double	f1dim (double x)
void	powell (double p[], double **xi, double ftol, int &iter, double &fret, int itermax=0)
void	linmin (double p[], double xi[], double &fret)
double	brent (double ax, double bx, double cx, double tol, double &xmin)
void	mnbrak (double *ax, double *bx, double *cx, double *fa, double *fb, double *fc)

Private Attributes
likelihood *	_Lptr
size_t	_ndim
int	_num_likelihood
Ran2RNG	_rng
double	_prior_edge_beta
int	_dof_estimate
double	_ko_ll_red_fac
size_t	_ko_itermax
size_t	_ko_rounds
double *	_pcom
double *	_xicom

Detailed Description

Maximizes a given likelihood using the tempered_powell method as described in Numerical Recipes in C (1992; Press, Teukoslky, Vetterling and Flannery). An attempt to generate a global maximum is made by starting many instances at random/specified locations. Multiple rounds of trial solutions of a fixed number of Powell iterations are generated and then compared to an estimate of the expected value. If too small, a new random point is chosen. This drives the optimizer to try increasingly better locations. To avoid pathological behavior at the prior limits, if present, the likelihood is "tempered" near the boundary by a function that drives the likelihood to zero to generate smooth maxima. (This does not appear to be particularly important.)

Todo:

Member Function Documentation

std::vector< double > generate_start_point ( likelihood &  L )

private

Parameters

P	vector of pointers to priors.

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double get_optimal_point ( std::vector< double > &  pvec, double  tolerance, int  itermax = 0  )

private

Parameters

pvec	vector of start parameter position, reset to the optimal point.
P	vector of pointers to priors.
tolerance	numerical tolerance at which to terminate maximization.

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std::vector< double > run_optimizer	(	likelihood &	L,
		int	dof_estimate,
		std::string	optimizer_results_filename = "Opt.dat",
		size_t	number_of_instances = 0,
		size_t	number_of_restarts = 2,
		double	tolerance = 1e-10
	)

Parameters

L	An object of class likelihood.
dof_estimate	An estimate of the number of expected degrees of freedom. Better estimates will produce better quality checks, but even bad estimates are very useful.
optimizer_results_filename	Name of file to which to write summary data. Default is Opt.dat.
number_of_instances	Number of independent realizations of the optimizer to run. When set to 0 will run the maximum allowed by the number of processes being used. Default is 0.
number_of_restarts	The number of times to restart each optimizer realization from the best point. Default 2.
tolerance	Convergence tolerance. Default 1e-15.

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std::vector< double > run_optimizer	(	likelihood &	L,
		int	dof_estimate,
		std::vector< double >	start_parameter_values,
		std::string	optimizer_results_filename = "Opt.dat",
		size_t	number_of_instances = 0,
		size_t	number_of_restarts = 2,
		double	tolerance = 1e-10
	)

Parameters

L	An object of class likelihood.
dof_estimate	An estimate of the number of expected degrees of freedom. Better estimates will produce better quality checks, but even bad estimates are very useful.
start_parameter_values	A vector of a single start point that we want to initialize a Powell optimization around.
optimizer_results_filename	Name of file to which to write summary data. Default is Opt.dat.
number_of_instances	Number of independent realizations of the optimizer to run. When set to 0 will run the maximum allowed by the number of processes being used. Default is 0.
number_of_restarts	The number of times to restart each optimizer realization from the best point. Default 2.
tolerance	Convergence tolerance. Default 1e-15.

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std::vector< double > run_optimizer	(	likelihood &	L,
		int	dof_estimate,
		std::vector< std::vector< double > >	start_parameter_values,
		std::string	optimizer_results_filename = "Opt.dat",
		size_t	number_of_restarts = 2,
		double	tolerance = 1e-10
	)

Parameters

L	An object of class likelihood.
dof_estimate	An estimate of the number of expected degrees of freedom. Better estimates will produce better quality checks, but even bad estimates are very useful.
start_parameter_values	A vector of start points at which to initialize every Powell optimization around. Note that this sets the number of instances explicitly.
optimizer_results_filename	Name of file to which to write summary data. Default is Opt.dat.
number_of_restarts	The number of times to restart each optimizer realization from the best point. Default 2.
tolerance	Convergence tolerance. Default 1e-15.

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void set_cpu_distribution

(

int

num_likelihood

)

Parameters

num_likelihood

number of threads allocated for each likelihood calculation.

void set_kickout_parameters	(	double	kickout_loglikelihood_reduction_factor = 10.0,
		size_t	kickout_itermax = 20,
		size_t	kickout_rounds = 20
	)

Parameters

kickout_loglikelihood_reduction_factor	Factor by which to multiply the negative of the number of degrees of freedom above which a maximized likelihood is acceptable. Default 10.
kickout_itermax	Number of Powell iterations before kickout assessment. Default 20.
kickout_rounds	Number of kickout rounds to perform. Default 20.

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The documentation for this class was generated from the following files:

• optimizing/optimizer_kickout_powell.h
• optimizing/optimizer_kickout_powell.cpp